Overview

Dataset statistics

Number of variables45
Number of observations9725
Missing cells9676
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory346.0 B

Variable types

Numeric19
Categorical23
DateTime1
Boolean2

Warnings

game_id has a high cardinality: 9725 distinct values High cardinality
date_string has a high cardinality: 1885 distinct values High cardinality
over_under_line has a high cardinality: 68 distinct values High cardinality
stadium has a high cardinality: 82 distinct values High cardinality
address has a high cardinality: 70 distinct values High cardinality
qb1 has a high cardinality: 411 distinct values High cardinality
qb2 has a high cardinality: 420 distinct values High cardinality
df_index is highly correlated with schedule_seasonHigh correlation
schedule_season is highly correlated with df_indexHigh correlation
elo1_pre is highly correlated with qbelo1_preHigh correlation
elo2_pre is highly correlated with qbelo2_preHigh correlation
elo_prob1 is highly correlated with elo_prob2 and 2 other fieldsHigh correlation
elo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo1_pre is highly correlated with elo1_preHigh correlation
qbelo2_pre is highly correlated with elo2_preHigh correlation
qbelo_prob1 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
home_team_id is highly correlated with address and 5 other fieldsHigh correlation
stadium_neutral is highly correlated with compass_homeHigh correlation
schedule_week is highly correlated with schedule_playoffHigh correlation
schedule_playoff is highly correlated with schedule_weekHigh correlation
address is highly correlated with home_team_id and 5 other fieldsHigh correlation
away_city is highly correlated with team2 and 3 other fieldsHigh correlation
home_teamname is highly correlated with home_team_id and 5 other fieldsHigh correlation
team_home is highly correlated with home_team_id and 5 other fieldsHigh correlation
team2 is highly correlated with away_city and 3 other fieldsHigh correlation
team_away is highly correlated with away_city and 3 other fieldsHigh correlation
stadium is highly correlated with home_team_id and 5 other fieldsHigh correlation
home_city is highly correlated with home_team_id and 5 other fieldsHigh correlation
away_teamname is highly correlated with away_city and 3 other fieldsHigh correlation
team1 is highly correlated with home_team_id and 5 other fieldsHigh correlation
compass_home is highly correlated with stadium_neutralHigh correlation
away_team_id is highly correlated with away_city and 3 other fieldsHigh correlation
compass_home has 9676 (99.5%) missing values Missing
dt_for_home is highly skewed (γ1 = 21.07824614) Skewed
bearing_home is highly skewed (γ1 = 21.69803775) Skewed
df_index is uniformly distributed Uniform
game_id is uniformly distributed Uniform
df_index has unique values Unique
game_id has unique values Unique
qbelo1_pre has unique values Unique
qbelo2_pre has unique values Unique
qbelo_prob1 has unique values Unique
qbelo_prob2 has unique values Unique
score_home has 115 (1.2%) zeros Zeros
score_away has 217 (2.2%) zeros Zeros
spread_favorite has 133 (1.4%) zeros Zeros
dt_for_home has 9676 (99.5%) zeros Zeros
bearing_home has 9676 (99.5%) zeros Zeros

Reproduction

Analysis started2021-04-16 16:00:34.988607
Analysis finished2021-04-16 16:01:24.922781
Duration49.93 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7160
Minimum2298
Maximum12022
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:24.992351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2298
5-th percentile2784.2
Q14729
median7160
Q39591
95-th percentile11535.8
Maximum12022
Range9724
Interquartile range (IQR)4862

Descriptive statistics

Standard deviation2807.510018
Coefficient of variation (CV)0.3921103377
Kurtosis-1.2
Mean7160
Median Absolute Deviation (MAD)2431
Skewness0
Sum69631000
Variance7882112.5
MonotocityStrictly increasing
2021-04-16T12:01:25.132277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40981
 
< 0.1%
27401
 
< 0.1%
47911
 
< 0.1%
109361
 
< 0.1%
88891
 
< 0.1%
27481
 
< 0.1%
68461
 
< 0.1%
47991
 
< 0.1%
109441
 
< 0.1%
88971
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
22981
< 0.1%
22991
< 0.1%
23001
< 0.1%
23011
< 0.1%
23021
< 0.1%
ValueCountFrequency (%)
120221
< 0.1%
120211
< 0.1%
120201
< 0.1%
120191
< 0.1%
120181
< 0.1%

game_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
20091011LARMIN
 
1
20150920PHIDAL
 
1
20060917SEAARI
 
1
20081026HOUCIN
 
1
19890910MINTEN
 
1
Other values (9720)
9720 

Length

Max length14
Median length14
Mean length13.58231362
Min length12

Characters and Unicode

Total characters132088
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9725 ?
Unique (%)100.0%

Sample

1st row19790901TBDET
2nd row19790902BUFMIA
3rd row19790902CHIGB
4th row19790902DENCIN
5th row19790902KCIND
ValueCountFrequency (%)
20091011LARMIN1
 
< 0.1%
20150920PHIDAL1
 
< 0.1%
20060917SEAARI1
 
< 0.1%
20081026HOUCIN1
 
< 0.1%
19890910MINTEN1
 
< 0.1%
19881009ATLLAR1
 
< 0.1%
20141019CHIMIA1
 
< 0.1%
20020915BALTB1
 
< 0.1%
20200920PITDEN1
 
< 0.1%
19971019BALMIA1
 
< 0.1%
Other values (9715)9715
99.9%
2021-04-16T12:01:25.415374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19891224noind1
 
< 0.1%
20100103denkc1
 
< 0.1%
19840909miane1
 
< 0.1%
20031221seaari1
 
< 0.1%
19981015detgb1
 
< 0.1%
19891008mindet1
 
< 0.1%
19830109minatl1
 
< 0.1%
19941211neind1
 
< 0.1%
19830904chiatl1
 
< 0.1%
19940918pitind1
 
< 0.1%
Other values (9715)9715
99.9%

Most occurring characters

ValueCountFrequency (%)
122292
16.9%
017629
13.3%
212962
 
9.8%
910927
 
8.3%
N6021
 
4.6%
A5838
 
4.4%
I5277
 
4.0%
L4324
 
3.3%
E4005
 
3.0%
83951
 
3.0%
Other values (23)38862
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77800
58.9%
Uppercase Letter54288
41.1%

Most frequent character per category

ValueCountFrequency (%)
N6021
11.1%
A5838
10.8%
I5277
 
9.7%
L4324
 
8.0%
E4005
 
7.4%
C3718
 
6.8%
T3333
 
6.1%
D2627
 
4.8%
B2437
 
4.5%
R2370
 
4.4%
Other values (13)14338
26.4%
ValueCountFrequency (%)
122292
28.7%
017629
22.7%
212962
16.7%
910927
14.0%
83951
 
5.1%
32361
 
3.0%
72060
 
2.6%
61913
 
2.5%
41895
 
2.4%
51810
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common77800
58.9%
Latin54288
41.1%

Most frequent character per script

ValueCountFrequency (%)
N6021
11.1%
A5838
10.8%
I5277
 
9.7%
L4324
 
8.0%
E4005
 
7.4%
C3718
 
6.8%
T3333
 
6.1%
D2627
 
4.8%
B2437
 
4.5%
R2370
 
4.4%
Other values (13)14338
26.4%
ValueCountFrequency (%)
122292
28.7%
017629
22.7%
212962
16.7%
910927
14.0%
83951
 
5.1%
32361
 
3.0%
72060
 
2.6%
61913
 
2.5%
41895
 
2.4%
51810
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII132088
100.0%

Most frequent character per block

ValueCountFrequency (%)
122292
16.9%
017629
13.3%
212962
 
9.8%
910927
 
8.3%
N6021
 
4.6%
A5838
 
4.4%
I5277
 
4.0%
L4324
 
3.3%
E4005
 
3.0%
83951
 
3.0%
Other values (23)38862
29.4%

date_string
Categorical

HIGH CARDINALITY

Distinct1885
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Sun Dec 31, 2006
 
15
Sun Jan 03, 2010
 
15
Sun Sep 15, 2002
 
15
Sun Dec 29, 2013
 
15
Sun Sep 25, 2011
 
15
Other values (1880)
9650 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters155600
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique909 ?
Unique (%)9.3%

Sample

1st rowSat Sep 01, 1979
2nd rowSun Sep 02, 1979
3rd rowSun Sep 02, 1979
4th rowSun Sep 02, 1979
5th rowSun Sep 02, 1979
ValueCountFrequency (%)
Sun Dec 31, 200615
 
0.2%
Sun Jan 03, 201015
 
0.2%
Sun Sep 15, 200215
 
0.2%
Sun Dec 29, 201315
 
0.2%
Sun Sep 25, 201115
 
0.2%
Sun Dec 29, 201915
 
0.2%
Sun Dec 28, 201415
 
0.2%
Sun Dec 30, 201215
 
0.2%
Sun Jan 03, 202115
 
0.2%
Sun Dec 30, 201815
 
0.2%
Other values (1875)9575
98.5%
2021-04-16T12:01:25.669923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sun8452
21.7%
dec2437
 
6.3%
nov2412
 
6.2%
oct2230
 
5.7%
sep2105
 
5.4%
mon639
 
1.6%
jan517
 
1.3%
09361
 
0.9%
20356
 
0.9%
13355
 
0.9%
Other values (78)19036
48.9%

Most occurring characters

ValueCountFrequency (%)
29175
18.8%
012753
 
8.2%
112284
 
7.9%
S10883
 
7.0%
210514
 
6.8%
,9725
 
6.2%
n9608
 
6.2%
98822
 
5.7%
u8762
 
5.6%
c4667
 
3.0%
Other values (27)38407
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number58350
37.5%
Lowercase Letter38900
25.0%
Space Separator29175
18.8%
Uppercase Letter19450
 
12.5%
Other Punctuation9725
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
n9608
24.7%
u8762
22.5%
c4667
12.0%
e4558
11.7%
o3051
 
7.8%
t2556
 
6.6%
v2412
 
6.2%
p2105
 
5.4%
a843
 
2.2%
h294
 
0.8%
Other values (5)44
 
0.1%
ValueCountFrequency (%)
S10883
56.0%
D2437
 
12.5%
N2412
 
12.4%
O2230
 
11.5%
M639
 
3.3%
J517
 
2.7%
T297
 
1.5%
F20
 
0.1%
A13
 
0.1%
W2
 
< 0.1%
ValueCountFrequency (%)
012753
21.9%
112284
21.1%
210514
18.0%
98822
15.1%
83938
 
6.7%
32361
 
4.0%
72060
 
3.5%
61913
 
3.3%
41895
 
3.2%
51810
 
3.1%
ValueCountFrequency (%)
29175
100.0%
ValueCountFrequency (%)
,9725
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common97250
62.5%
Latin58350
37.5%

Most frequent character per script

ValueCountFrequency (%)
S10883
18.7%
n9608
16.5%
u8762
15.0%
c4667
8.0%
e4558
7.8%
o3051
 
5.2%
t2556
 
4.4%
D2437
 
4.2%
N2412
 
4.1%
v2412
 
4.1%
Other values (15)7004
12.0%
ValueCountFrequency (%)
29175
30.0%
012753
13.1%
112284
12.6%
210514
 
10.8%
,9725
 
10.0%
98822
 
9.1%
83938
 
4.0%
32361
 
2.4%
72060
 
2.1%
61913
 
2.0%
Other values (2)3705
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII155600
100.0%

Most frequent character per block

ValueCountFrequency (%)
29175
18.8%
012753
 
8.2%
112284
 
7.9%
S10883
 
7.0%
210514
 
6.8%
,9725
 
6.2%
n9608
 
6.2%
98822
 
5.7%
u8762
 
5.6%
c4667
 
3.0%
Other values (27)38407
24.7%
Distinct1885
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Minimum1979-09-01 00:00:00
Maximum2021-02-07 00:00:00
2021-04-16T12:01:25.778851image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:25.919605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

schedule_season
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.462211
Minimum1979
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:26.050495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1981
Q11990
median2001
Q32011
95-th percentile2019
Maximum2020
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation11.97667314
Coefficient of variation (CV)0.005986952954
Kurtosis-1.149304517
Mean2000.462211
Median Absolute Deviation (MAD)10
Skewness-0.1079057553
Sum19454495
Variance143.4406996
MonotocityIncreasing
2021-04-16T12:01:26.168474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2020252
 
2.6%
2018251
 
2.6%
2017251
 
2.6%
2004251
 
2.6%
2019251
 
2.6%
2006251
 
2.6%
2003250
 
2.6%
2002250
 
2.6%
2015250
 
2.6%
2016250
 
2.6%
Other values (32)7218
74.2%
ValueCountFrequency (%)
1979217
2.2%
1980217
2.2%
1981217
2.2%
1982128
1.3%
1983214
2.2%
ValueCountFrequency (%)
2020252
2.6%
2019251
2.6%
2018251
2.6%
2017251
2.6%
2016250
2.6%

schedule_week
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
15
 
591
16
 
590
2
 
589
1
 
589
13
 
589
Other values (18)
6777 

Length

Max length10
Median length2
Mean length1.775424165
Min length1

Characters and Unicode

Total characters17266
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
15591
 
6.1%
16590
 
6.1%
2589
 
6.1%
1589
 
6.1%
13589
 
6.1%
14587
 
6.0%
12584
 
6.0%
11571
 
5.9%
3543
 
5.6%
10537
 
5.5%
Other values (13)3955
40.7%
2021-04-16T12:01:26.398253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15591
 
6.1%
16590
 
6.1%
2589
 
6.1%
1589
 
6.1%
13589
 
6.1%
14587
 
6.0%
12584
 
6.0%
11571
 
5.9%
3543
 
5.6%
10537
 
5.5%
Other values (12)3955
40.7%

Most occurring characters

ValueCountFrequency (%)
15683
32.9%
21173
 
6.8%
31132
 
6.6%
41105
 
6.4%
51100
 
6.4%
61093
 
6.3%
7975
 
5.6%
i617
 
3.6%
0537
 
3.1%
8525
 
3.0%
Other values (20)3326
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13843
80.2%
Lowercase Letter3014
 
17.5%
Uppercase Letter409
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
i617
20.5%
n316
10.5%
d286
9.5%
o262
8.7%
e262
8.7%
r247
8.2%
c218
 
7.2%
l168
 
5.6%
v158
 
5.2%
s158
 
5.2%
Other values (6)322
10.7%
ValueCountFrequency (%)
15683
41.1%
21173
 
8.5%
31132
 
8.2%
41105
 
8.0%
51100
 
7.9%
61093
 
7.9%
7975
 
7.0%
0537
 
3.9%
8525
 
3.8%
9520
 
3.8%
ValueCountFrequency (%)
D158
38.6%
W143
35.0%
C83
20.3%
S25
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common13843
80.2%
Latin3423
 
19.8%

Most frequent character per script

ValueCountFrequency (%)
i617
18.0%
n316
9.2%
d286
 
8.4%
o262
 
7.7%
e262
 
7.7%
r247
 
7.2%
c218
 
6.4%
l168
 
4.9%
D158
 
4.6%
v158
 
4.6%
Other values (10)731
21.4%
ValueCountFrequency (%)
15683
41.1%
21173
 
8.5%
31132
 
8.2%
41105
 
8.0%
51100
 
7.9%
61093
 
7.9%
7975
 
7.0%
0537
 
3.9%
8525
 
3.8%
9520
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII17266
100.0%

Most frequent character per block

ValueCountFrequency (%)
15683
32.9%
21173
 
6.8%
31132
 
6.6%
41105
 
6.4%
51100
 
6.4%
61093
 
6.3%
7975
 
5.6%
i617
 
3.6%
0537
 
3.1%
8525
 
3.0%
Other values (20)3326
19.3%

schedule_playoff
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9320 
True
 
405
ValueCountFrequency (%)
False9320
95.8%
True405
 
4.2%
2021-04-16T12:01:26.458249image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

home_team_id
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
NE
 
355
SF
 
351
DEN
 
348
PIT
 
347
GB
 
343
Other values (26)
7981 

Length

Max length3
Median length3
Mean length2.789820051
Min length2

Characters and Unicode

Total characters27131
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowBUF
3rd rowCHI
4th rowDEN
5th rowKC
ValueCountFrequency (%)
NE355
 
3.7%
SF351
 
3.6%
DEN348
 
3.6%
PIT347
 
3.6%
GB343
 
3.5%
KC341
 
3.5%
MIA340
 
3.5%
LVR338
 
3.5%
IND338
 
3.5%
SEA336
 
3.5%
Other values (21)6288
64.7%
2021-04-16T12:01:26.759395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne355
 
3.7%
sf351
 
3.6%
den348
 
3.6%
pit347
 
3.6%
gb343
 
3.5%
kc341
 
3.5%
mia340
 
3.5%
lvr338
 
3.5%
ind338
 
3.5%
ten336
 
3.5%
Other values (21)6288
64.7%

Most occurring characters

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27131
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring scripts

ValueCountFrequency (%)
Latin27131
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII27131
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

home_city
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
New York
 
634
Foxborough
 
355
San Francisco
 
351
Denver
 
348
Pittsburgh
 
347
Other values (27)
7690 

Length

Max length13
Median length9
Mean length8.863856041
Min length5

Characters and Unicode

Total characters86201
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTampa Bay
2nd rowBuffalo
3rd rowChicago
4th rowDenver
5th rowKansas City
ValueCountFrequency (%)
New York634
 
6.5%
Foxborough355
 
3.7%
San Francisco351
 
3.6%
Denver348
 
3.6%
Pittsburgh347
 
3.6%
Green Bay343
 
3.5%
Kansas City341
 
3.5%
Miami340
 
3.5%
Cincinnati336
 
3.5%
Seattle336
 
3.5%
Other values (22)5994
61.6%
2021-04-16T12:01:26.988183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new964
 
7.5%
bay667
 
5.2%
san656
 
5.1%
york634
 
4.9%
foxborough355
 
2.8%
francisco351
 
2.7%
denver348
 
2.7%
pittsburgh347
 
2.7%
green343
 
2.7%
city341
 
2.6%
Other values (29)7876
61.1%

Most occurring characters

ValueCountFrequency (%)
a9284
 
10.8%
e7515
 
8.7%
n6946
 
8.1%
i6498
 
7.5%
o6004
 
7.0%
l5211
 
6.0%
t4523
 
5.2%
s4382
 
5.1%
r3480
 
4.0%
3157
 
3.7%
Other values (34)29201
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter69939
81.1%
Uppercase Letter12882
 
14.9%
Space Separator3157
 
3.7%
Other Punctuation223
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
a9284
13.3%
e7515
10.7%
n6946
9.9%
i6498
9.3%
o6004
8.6%
l5211
 
7.5%
t4523
 
6.5%
s4382
 
6.3%
r3480
 
5.0%
h2113
 
3.0%
Other values (13)13983
20.0%
ValueCountFrequency (%)
C1526
11.8%
D1286
10.0%
B1245
9.7%
S1215
 
9.4%
N964
 
7.5%
P906
 
7.0%
F706
 
5.5%
M673
 
5.2%
Y634
 
4.9%
A626
 
4.9%
Other values (9)3101
24.1%
ValueCountFrequency (%)
3157
100.0%
ValueCountFrequency (%)
.223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin82821
96.1%
Common3380
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
a9284
 
11.2%
e7515
 
9.1%
n6946
 
8.4%
i6498
 
7.8%
o6004
 
7.2%
l5211
 
6.3%
t4523
 
5.5%
s4382
 
5.3%
r3480
 
4.2%
h2113
 
2.6%
Other values (32)26865
32.4%
ValueCountFrequency (%)
3157
93.4%
.223
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII86201
100.0%

Most frequent character per block

ValueCountFrequency (%)
a9284
 
10.8%
e7515
 
8.7%
n6946
 
8.1%
i6498
 
7.5%
o6004
 
7.0%
l5211
 
6.0%
t4523
 
5.2%
s4382
 
5.1%
r3480
 
4.0%
3157
 
3.7%
Other values (34)29201
33.9%

home_teamname
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Patriots
 
355
49ers
 
351
Broncos
 
348
Steelers
 
347
Packers
 
343
Other values (27)
7981 

Length

Max length10
Median length7
Mean length6.57562982
Min length4

Characters and Unicode

Total characters63948
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuccaneers
2nd rowBills
3rd rowBears
4th rowBroncos
5th rowChiefs
ValueCountFrequency (%)
Patriots355
 
3.7%
49ers351
 
3.6%
Broncos348
 
3.6%
Steelers347
 
3.6%
Packers343
 
3.5%
Chiefs341
 
3.5%
Dolphins340
 
3.5%
Colts338
 
3.5%
Raiders338
 
3.5%
Bengals336
 
3.5%
Other values (22)6288
64.7%
2021-04-16T12:01:27.232837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
patriots355
 
3.7%
49ers351
 
3.6%
broncos348
 
3.6%
steelers347
 
3.6%
packers343
 
3.5%
chiefs341
 
3.5%
dolphins340
 
3.5%
raiders338
 
3.5%
colts338
 
3.5%
bengals336
 
3.5%
Other values (22)6288
64.7%

Most occurring characters

ValueCountFrequency (%)
s9725
15.2%
a6408
 
10.0%
e5773
 
9.0%
r4596
 
7.2%
n4325
 
6.8%
i3972
 
6.2%
o3309
 
5.2%
l3135
 
4.9%
t2745
 
4.3%
B1985
 
3.1%
Other values (29)17975
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53872
84.2%
Uppercase Letter9374
 
14.7%
Decimal Number702
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
s9725
18.1%
a6408
11.9%
e5773
10.7%
r4596
8.5%
n4325
8.0%
i3972
7.4%
o3309
 
6.1%
l3135
 
5.8%
t2745
 
5.1%
c1667
 
3.1%
Other values (13)8217
15.3%
ValueCountFrequency (%)
B1985
21.2%
C1631
17.4%
S1013
10.8%
P906
9.7%
R873
9.3%
J537
 
5.7%
D340
 
3.6%
T334
 
3.6%
V333
 
3.6%
F328
 
3.5%
Other values (4)1094
11.7%
ValueCountFrequency (%)
4351
50.0%
9351
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63246
98.9%
Common702
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
s9725
15.4%
a6408
 
10.1%
e5773
 
9.1%
r4596
 
7.3%
n4325
 
6.8%
i3972
 
6.3%
o3309
 
5.2%
l3135
 
5.0%
t2745
 
4.3%
B1985
 
3.1%
Other values (27)17273
27.3%
ValueCountFrequency (%)
4351
50.0%
9351
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63948
100.0%

Most frequent character per block

ValueCountFrequency (%)
s9725
15.2%
a6408
 
10.0%
e5773
 
9.0%
r4596
 
7.2%
n4325
 
6.8%
i3972
 
6.2%
o3309
 
5.2%
l3135
 
4.9%
t2745
 
4.3%
B1985
 
3.1%
Other values (29)17975
28.1%

away_city
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
New York
 
643
Green Bay
 
346
Pittsburgh
 
344
Foxborough
 
343
Kansas City
 
339
Other values (27)
7710 

Length

Max length13
Median length9
Mean length8.863033419
Min length5

Characters and Unicode

Total characters86193
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDetroit
2nd rowMiami
3rd rowGreen Bay
4th rowCincinnati
5th rowBaltimore
ValueCountFrequency (%)
New York643
 
6.6%
Green Bay346
 
3.6%
Pittsburgh344
 
3.5%
Foxborough343
 
3.5%
Kansas City339
 
3.5%
Buffalo339
 
3.5%
Seattle339
 
3.5%
Denver338
 
3.5%
Minneapolis337
 
3.5%
Miami335
 
3.4%
Other values (22)6022
61.9%
2021-04-16T12:01:27.469148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new973
 
7.6%
bay676
 
5.2%
york643
 
5.0%
san634
 
4.9%
green346
 
2.7%
pittsburgh344
 
2.7%
foxborough343
 
2.7%
buffalo339
 
2.6%
seattle339
 
2.6%
city339
 
2.6%
Other values (29)7910
61.4%

Most occurring characters

ValueCountFrequency (%)
a9263
 
10.7%
e7589
 
8.8%
n6941
 
8.1%
i6472
 
7.5%
o5989
 
6.9%
l5242
 
6.1%
t4540
 
5.3%
s4396
 
5.1%
r3456
 
4.0%
3161
 
3.7%
Other values (34)29144
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter69921
81.1%
Uppercase Letter12886
 
15.0%
Space Separator3161
 
3.7%
Other Punctuation225
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
a9263
13.2%
e7589
10.9%
n6941
9.9%
i6472
9.3%
o5989
8.6%
l5242
 
7.5%
t4540
 
6.5%
s4396
 
6.3%
r3456
 
4.9%
h2084
 
3.0%
Other values (13)13949
19.9%
ValueCountFrequency (%)
C1518
11.8%
D1269
9.8%
B1264
9.8%
S1198
 
9.3%
N973
 
7.6%
P900
 
7.0%
F673
 
5.2%
M672
 
5.2%
Y643
 
5.0%
A639
 
5.0%
Other values (9)3137
24.3%
ValueCountFrequency (%)
3161
100.0%
ValueCountFrequency (%)
.225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin82807
96.1%
Common3386
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
a9263
 
11.2%
e7589
 
9.2%
n6941
 
8.4%
i6472
 
7.8%
o5989
 
7.2%
l5242
 
6.3%
t4540
 
5.5%
s4396
 
5.3%
r3456
 
4.2%
h2084
 
2.5%
Other values (32)26835
32.4%
ValueCountFrequency (%)
3161
93.4%
.225
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII86193
100.0%

Most frequent character per block

ValueCountFrequency (%)
a9263
 
10.7%
e7589
 
8.8%
n6941
 
8.1%
i6472
 
7.5%
o5989
 
6.9%
l5242
 
6.1%
t4540
 
5.3%
s4396
 
5.1%
r3456
 
4.0%
3161
 
3.7%
Other values (34)29144
33.8%

away_teamname
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Packers
 
346
Steelers
 
344
Patriots
 
343
Colts
 
343
Jets
 
342
Other values (27)
8007 

Length

Max length10
Median length7
Mean length6.574293059
Min length4

Characters and Unicode

Total characters63935
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLions
2nd rowDolphins
3rd rowPackers
4th rowBengals
5th rowColts
ValueCountFrequency (%)
Packers346
 
3.6%
Steelers344
 
3.5%
Patriots343
 
3.5%
Colts343
 
3.5%
Jets342
 
3.5%
Chiefs339
 
3.5%
Bills339
 
3.5%
Seahawks339
 
3.5%
Chargers338
 
3.5%
Broncos338
 
3.5%
Other values (22)6314
64.9%
2021-04-16T12:01:27.725842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
packers346
 
3.6%
steelers344
 
3.5%
patriots343
 
3.5%
colts343
 
3.5%
jets342
 
3.5%
chiefs339
 
3.5%
seahawks339
 
3.5%
bills339
 
3.5%
chargers338
 
3.5%
broncos338
 
3.5%
Other values (22)6314
64.9%

Most occurring characters

ValueCountFrequency (%)
s9725
15.2%
a6445
 
10.1%
e5758
 
9.0%
r4573
 
7.2%
n4342
 
6.8%
i3974
 
6.2%
o3271
 
5.1%
l3141
 
4.9%
t2740
 
4.3%
B1977
 
3.1%
Other values (29)17989
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53880
84.3%
Uppercase Letter9395
 
14.7%
Decimal Number660
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
s9725
18.0%
a6445
12.0%
e5758
10.7%
r4573
8.5%
n4342
8.1%
i3974
7.4%
o3271
 
6.1%
l3141
 
5.8%
t2740
 
5.1%
c1677
 
3.1%
Other values (13)8234
15.3%
ValueCountFrequency (%)
B1977
21.0%
C1634
17.4%
S1013
10.8%
P898
9.6%
R883
9.4%
J557
 
5.9%
T338
 
3.6%
V337
 
3.6%
D335
 
3.6%
F333
 
3.5%
Other values (4)1090
11.6%
ValueCountFrequency (%)
4330
50.0%
9330
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63275
99.0%
Common660
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
s9725
15.4%
a6445
 
10.2%
e5758
 
9.1%
r4573
 
7.2%
n4342
 
6.9%
i3974
 
6.3%
o3271
 
5.2%
l3141
 
5.0%
t2740
 
4.3%
B1977
 
3.1%
Other values (27)17329
27.4%
ValueCountFrequency (%)
4330
50.0%
9330
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63935
100.0%

Most frequent character per block

ValueCountFrequency (%)
s9725
15.2%
a6445
 
10.1%
e5758
 
9.0%
r4573
 
7.2%
n4342
 
6.8%
i3974
 
6.2%
o3271
 
5.1%
l3141
 
4.9%
t2740
 
4.3%
B1977
 
3.1%
Other values (29)17989
28.1%

away_team_id
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
GB
 
346
TEN
 
345
PIT
 
344
IND
 
343
NE
 
343
Other values (26)
8004 

Length

Max length3
Median length3
Mean length2.792493573
Min length2

Characters and Unicode

Total characters27157
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDET
2nd rowMIA
3rd rowGB
4th rowCIN
5th rowIND
ValueCountFrequency (%)
GB346
 
3.6%
TEN345
 
3.5%
PIT344
 
3.5%
IND343
 
3.5%
NE343
 
3.5%
NYJ342
 
3.5%
SEA339
 
3.5%
KC339
 
3.5%
BUF339
 
3.5%
DEN338
 
3.5%
Other values (21)6307
64.9%
2021-04-16T12:01:27.968082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb346
 
3.6%
ten345
 
3.5%
pit344
 
3.5%
ne343
 
3.5%
ind343
 
3.5%
nyj342
 
3.5%
sea339
 
3.5%
kc339
 
3.5%
buf339
 
3.5%
den338
 
3.5%
Other values (21)6307
64.9%

Most occurring characters

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27157
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring scripts

ValueCountFrequency (%)
Latin27157
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII27157
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

result
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
1
5637 
0
4088 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9725
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
15637
58.0%
04088
42.0%
2021-04-16T12:01:28.158905image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:01:28.217936image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring characters

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9725
100.0%

Most frequent character per category

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common9725
100.0%

Most frequent character per script

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9725
100.0%

Most frequent character per block

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

team_home
Categorical

HIGH CORRELATION

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
New England Patriots
 
355
San Francisco 49ers
 
351
Denver Broncos
 
348
Pittsburgh Steelers
 
347
Green Bay Packers
 
343
Other values (35)
7981 

Length

Max length20
Median length16
Mean length16.38611825
Min length13

Characters and Unicode

Total characters159355
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTampa Bay Buccaneers
2nd rowBuffalo Bills
3rd rowChicago Bears
4th rowDenver Broncos
5th rowKansas City Chiefs
ValueCountFrequency (%)
New England Patriots355
 
3.7%
San Francisco 49ers351
 
3.6%
Denver Broncos348
 
3.6%
Pittsburgh Steelers347
 
3.6%
Green Bay Packers343
 
3.5%
Kansas City Chiefs341
 
3.5%
Miami Dolphins340
 
3.5%
Cincinnati Bengals336
 
3.5%
Seattle Seahawks336
 
3.5%
Buffalo Bills336
 
3.5%
Other values (30)6292
64.7%
2021-04-16T12:01:28.436554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1319
 
5.7%
bay667
 
2.9%
san656
 
2.9%
york634
 
2.8%
england355
 
1.5%
patriots355
 
1.5%
francisco351
 
1.5%
49ers351
 
1.5%
broncos348
 
1.5%
denver348
 
1.5%
Other values (62)17578
76.6%

Most occurring characters

ValueCountFrequency (%)
a16461
 
10.3%
s14107
 
8.9%
13237
 
8.3%
e13229
 
8.3%
n12189
 
7.6%
i10345
 
6.5%
l8368
 
5.3%
o8248
 
5.2%
r7927
 
5.0%
t7185
 
4.5%
Other values (39)48059
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter122582
76.9%
Uppercase Letter22611
 
14.2%
Space Separator13237
 
8.3%
Decimal Number702
 
0.4%
Other Punctuation223
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a16461
13.4%
s14107
11.5%
e13229
10.8%
n12189
9.9%
i10345
8.4%
l8368
 
6.8%
o8248
 
6.7%
r7927
 
6.5%
t7185
 
5.9%
c3244
 
2.6%
Other values (14)21279
17.4%
ValueCountFrequency (%)
B3230
14.3%
C3157
14.0%
S2228
 
9.9%
D1626
 
7.2%
P1606
 
7.1%
N1319
 
5.8%
R873
 
3.9%
T851
 
3.8%
L851
 
3.8%
A832
 
3.7%
Other values (11)6038
26.7%
ValueCountFrequency (%)
4351
50.0%
9351
50.0%
ValueCountFrequency (%)
13237
100.0%
ValueCountFrequency (%)
.223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin145193
91.1%
Common14162
 
8.9%

Most frequent character per script

ValueCountFrequency (%)
a16461
 
11.3%
s14107
 
9.7%
e13229
 
9.1%
n12189
 
8.4%
i10345
 
7.1%
l8368
 
5.8%
o8248
 
5.7%
r7927
 
5.5%
t7185
 
4.9%
c3244
 
2.2%
Other values (35)43890
30.2%
ValueCountFrequency (%)
13237
93.5%
4351
 
2.5%
9351
 
2.5%
.223
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII159355
100.0%

Most frequent character per block

ValueCountFrequency (%)
a16461
 
10.3%
s14107
 
8.9%
13237
 
8.3%
e13229
 
8.3%
n12189
 
7.6%
i10345
 
6.5%
l8368
 
5.3%
o8248
 
5.2%
r7927
 
5.0%
t7185
 
4.5%
Other values (39)48059
30.2%

score_home
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.78848329
Minimum0
Maximum62
Zeros115
Zeros (%)1.2%
Memory size76.1 KiB
2021-04-16T12:01:28.556971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q116
median23
Q330
95-th percentile41
Maximum62
Range62
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.3883454
Coefficient of variation (CV)0.4558594473
Kurtosis-0.00565199382
Mean22.78848329
Median Absolute Deviation (MAD)7
Skewness0.3004273429
Sum221618
Variance107.9177201
MonotocityNot monotonic
2021-04-16T12:01:28.682546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20687
 
7.1%
17667
 
6.9%
24654
 
6.7%
27588
 
6.0%
31483
 
5.0%
13460
 
4.7%
10450
 
4.6%
23420
 
4.3%
14397
 
4.1%
21393
 
4.0%
Other values (50)4526
46.5%
ValueCountFrequency (%)
0115
1.2%
21
 
< 0.1%
3172
1.8%
55
 
0.1%
6169
1.7%
ValueCountFrequency (%)
623
< 0.1%
612
< 0.1%
594
< 0.1%
584
< 0.1%
572
< 0.1%

score_away
Real number (ℝ≥0)

ZEROS

Distinct55
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.98879177
Minimum0
Maximum59
Zeros217
Zeros (%)2.2%
Memory size76.1 KiB
2021-04-16T12:01:28.912627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113
median20
Q327
95-th percentile38
Maximum59
Range59
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.10049147
Coefficient of variation (CV)0.5053077534
Kurtosis-0.2161275132
Mean19.98879177
Median Absolute Deviation (MAD)7
Skewness0.3025776928
Sum194391
Variance102.0199278
MonotocityNot monotonic
2021-04-16T12:01:29.036566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17763
 
7.8%
10651
 
6.7%
24619
 
6.4%
20610
 
6.3%
13523
 
5.4%
14519
 
5.3%
7444
 
4.6%
27437
 
4.5%
21436
 
4.5%
23432
 
4.4%
Other values (45)4291
44.1%
ValueCountFrequency (%)
0217
2.2%
24
 
< 0.1%
3310
3.2%
56
 
0.1%
6232
2.4%
ValueCountFrequency (%)
592
 
< 0.1%
562
 
< 0.1%
555
0.1%
541
 
< 0.1%
522
 
< 0.1%

team_away
Categorical

HIGH CORRELATION

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Green Bay Packers
 
346
Pittsburgh Steelers
 
344
New England Patriots
 
343
New York Jets
 
342
Seattle Seahawks
 
339
Other values (35)
8011 

Length

Max length20
Median length16
Mean length16.38179949
Min length13

Characters and Unicode

Total characters159313
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDetroit Lions
2nd rowMiami Dolphins
3rd rowGreen Bay Packers
4th rowCincinnati Bengals
5th rowBaltimore Colts
ValueCountFrequency (%)
Green Bay Packers346
 
3.6%
Pittsburgh Steelers344
 
3.5%
New England Patriots343
 
3.5%
New York Jets342
 
3.5%
Seattle Seahawks339
 
3.5%
Kansas City Chiefs339
 
3.5%
Buffalo Bills339
 
3.5%
Denver Broncos338
 
3.5%
Minnesota Vikings337
 
3.5%
Miami Dolphins335
 
3.4%
Other values (30)6323
65.0%
2021-04-16T12:01:29.310535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1316
 
5.7%
bay676
 
2.9%
york643
 
2.8%
san634
 
2.8%
packers346
 
1.5%
green346
 
1.5%
steelers344
 
1.5%
pittsburgh344
 
1.5%
colts343
 
1.5%
patriots343
 
1.5%
Other values (62)17619
76.8%

Most occurring characters

ValueCountFrequency (%)
a16469
 
10.3%
s14121
 
8.9%
e13272
 
8.3%
13229
 
8.3%
n12178
 
7.6%
i10318
 
6.5%
l8389
 
5.3%
o8231
 
5.2%
r7895
 
5.0%
t7199
 
4.5%
Other values (39)48012
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter122575
76.9%
Uppercase Letter22624
 
14.2%
Space Separator13229
 
8.3%
Decimal Number660
 
0.4%
Other Punctuation225
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a16469
13.4%
s14121
11.5%
e13272
10.8%
n12178
9.9%
i10318
8.4%
l8389
 
6.8%
o8231
 
6.7%
r7895
 
6.4%
t7199
 
5.9%
c3211
 
2.6%
Other values (14)21292
17.4%
ValueCountFrequency (%)
B3241
14.3%
C3152
13.9%
S2211
 
9.8%
D1604
 
7.1%
P1589
 
7.0%
N1316
 
5.8%
R883
 
3.9%
T869
 
3.8%
L863
 
3.8%
A848
 
3.7%
Other values (11)6048
26.7%
ValueCountFrequency (%)
4330
50.0%
9330
50.0%
ValueCountFrequency (%)
13229
100.0%
ValueCountFrequency (%)
.225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin145199
91.1%
Common14114
 
8.9%

Most frequent character per script

ValueCountFrequency (%)
a16469
 
11.3%
s14121
 
9.7%
e13272
 
9.1%
n12178
 
8.4%
i10318
 
7.1%
l8389
 
5.8%
o8231
 
5.7%
r7895
 
5.4%
t7199
 
5.0%
B3241
 
2.2%
Other values (35)43886
30.2%
ValueCountFrequency (%)
13229
93.7%
4330
 
2.3%
9330
 
2.3%
.225
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII159313
100.0%

Most frequent character per block

ValueCountFrequency (%)
a16469
 
10.3%
s14121
 
8.9%
e13272
 
8.3%
13229
 
8.3%
n12178
 
7.6%
i10318
 
6.5%
l8389
 
5.3%
o8231
 
5.2%
r7895
 
5.0%
t7199
 
4.5%
Other values (39)48012
30.1%

team_favorite_id
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
PIT
 
455
NE
 
445
DEN
 
420
SF
 
417
GB
 
401
Other values (27)
7587 

Length

Max length4
Median length3
Mean length2.788688946
Min length2

Characters and Unicode

Total characters27120
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowMIA
3rd rowCHI
4th rowDEN
5th rowKC
ValueCountFrequency (%)
PIT455
 
4.7%
NE445
 
4.6%
DEN420
 
4.3%
SF417
 
4.3%
GB401
 
4.1%
DAL385
 
4.0%
MIN367
 
3.8%
MIA351
 
3.6%
NO343
 
3.5%
PHI342
 
3.5%
Other values (22)5799
59.6%
2021-04-16T12:01:29.552435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pit455
 
4.7%
ne445
 
4.6%
den420
 
4.3%
sf417
 
4.3%
gb401
 
4.1%
dal385
 
4.0%
min367
 
3.8%
mia351
 
3.6%
no343
 
3.5%
phi342
 
3.5%
Other values (22)5799
59.6%

Most occurring characters

ValueCountFrequency (%)
N3048
11.2%
A2827
 
10.4%
I2739
 
10.1%
L2116
 
7.8%
E1981
 
7.3%
C1788
 
6.6%
T1547
 
5.7%
D1352
 
5.0%
B1199
 
4.4%
R1034
 
3.8%
Other values (13)7489
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27120
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3048
11.2%
A2827
 
10.4%
I2739
 
10.1%
L2116
 
7.8%
E1981
 
7.3%
C1788
 
6.6%
T1547
 
5.7%
D1352
 
5.0%
B1199
 
4.4%
R1034
 
3.8%
Other values (13)7489
27.6%

Most occurring scripts

ValueCountFrequency (%)
Latin27120
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3048
11.2%
A2827
 
10.4%
I2739
 
10.1%
L2116
 
7.8%
E1981
 
7.3%
C1788
 
6.6%
T1547
 
5.7%
D1352
 
5.0%
B1199
 
4.4%
R1034
 
3.8%
Other values (13)7489
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII27120
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3048
11.2%
A2827
 
10.4%
I2739
 
10.1%
L2116
 
7.8%
E1981
 
7.3%
C1788
 
6.6%
T1547
 
5.7%
D1352
 
5.0%
B1199
 
4.4%
R1034
 
3.8%
Other values (13)7489
27.6%

spread_favorite
Real number (ℝ)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.394190231
Minimum-26.5
Maximum0
Zeros133
Zeros (%)1.4%
Memory size76.1 KiB
2021-04-16T12:01:29.667494image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-26.5
5-th percentile-12.5
Q1-7
median-4.5
Q3-3
95-th percentile-1
Maximum0
Range26.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.430153417
Coefficient of variation (CV)-0.6358977473
Kurtosis1.163035489
Mean-5.394190231
Median Absolute Deviation (MAD)2
Skewness-1.066664965
Sum-52458.5
Variance11.76595246
MonotocityNot monotonic
2021-04-16T12:01:29.800048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
-31492
15.3%
-3.5763
 
7.8%
-7701
 
7.2%
-2.5626
 
6.4%
-6532
 
5.5%
-4530
 
5.4%
-6.5489
 
5.0%
-1472
 
4.9%
-2421
 
4.3%
-4.5342
 
3.5%
Other values (37)3357
34.5%
ValueCountFrequency (%)
-26.51
< 0.1%
-24.51
< 0.1%
-241
< 0.1%
-22.51
< 0.1%
-21.51
< 0.1%
ValueCountFrequency (%)
0133
 
1.4%
-1472
4.9%
-1.5302
3.1%
-2421
4.3%
-2.5626
6.4%

over_under_line
Categorical

HIGH CARDINALITY

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
41
 
564
44
 
511
42
 
507
43
 
487
37
 
474
Other values (63)
7182 

Length

Max length4
Median length2
Mean length2.692133676
Min length1

Characters and Unicode

Total characters26181
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row30
2nd row39
3rd row31
4th row31.5
5th row37
ValueCountFrequency (%)
41564
 
5.8%
44511
 
5.3%
42507
 
5.2%
43487
 
5.0%
37474
 
4.9%
40470
 
4.8%
38451
 
4.6%
39425
 
4.4%
45410
 
4.2%
43.5309
 
3.2%
Other values (58)5117
52.6%
2021-04-16T12:01:30.061083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41564
 
5.8%
44511
 
5.3%
42507
 
5.2%
43487
 
5.0%
37474
 
4.9%
40470
 
4.9%
38451
 
4.7%
39425
 
4.4%
45410
 
4.2%
43.5309
 
3.2%
Other values (57)5062
52.3%

Most occurring characters

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22733
86.8%
Other Punctuation3393
 
13.0%
Space Separator55
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
46952
30.6%
54941
21.7%
34136
18.2%
71156
 
5.1%
8993
 
4.4%
1989
 
4.4%
6975
 
4.3%
2889
 
3.9%
0860
 
3.8%
9842
 
3.7%
ValueCountFrequency (%)
.3393
100.0%
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26181
100.0%

Most frequent character per script

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII26181
100.0%

Most frequent character per block

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

stadium
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Metlife Stadium
 
468
Lambeau Field
 
342
Arrowhead Stadium
 
339
Soldier Field
 
323
Qualcomm Stadium
 
306
Other values (77)
7947 

Length

Max length35
Median length16
Mean length17.3888946
Min length8

Characters and Unicode

Total characters169107
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowHoulihan's Stadium
2nd rowRalph Wilson Stadium
3rd rowSoldier Field
4th rowMile High Stadium
5th rowArrowhead Stadium
ValueCountFrequency (%)
Metlife Stadium468
 
4.8%
Lambeau Field342
 
3.5%
Arrowhead Stadium339
 
3.5%
Soldier Field323
 
3.3%
Qualcomm Stadium306
 
3.1%
Louisiana Superdome293
 
3.0%
Candlestick Park290
 
3.0%
Ralph Wilson Stadium289
 
3.0%
Sun Life Stadium240
 
2.5%
Oakland Coliseum221
 
2.3%
Other values (72)6614
68.0%
2021-04-16T12:01:30.311973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium5503
22.7%
field1889
 
7.8%
bank649
 
2.7%
metlife634
 
2.6%
dome565
 
2.3%
of444
 
1.8%
coliseum355
 
1.5%
mile345
 
1.4%
high345
 
1.4%
lambeau342
 
1.4%
Other values (110)13215
54.4%

Most occurring characters

ValueCountFrequency (%)
i15414
 
9.1%
14561
 
8.6%
e13471
 
8.0%
a13387
 
7.9%
d10475
 
6.2%
t10045
 
5.9%
m9691
 
5.7%
u9176
 
5.4%
o7951
 
4.7%
l7586
 
4.5%
Other values (44)57350
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter127766
75.6%
Uppercase Letter25762
 
15.2%
Space Separator14561
 
8.6%
Other Punctuation715
 
0.4%
Dash Punctuation155
 
0.1%
Open Punctuation74
 
< 0.1%
Close Punctuation74
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i15414
12.1%
e13471
10.5%
a13387
10.5%
d10475
8.2%
t10045
7.9%
m9691
7.6%
u9176
 
7.2%
o7951
 
6.2%
l7586
 
5.9%
n6433
 
5.0%
Other values (14)24137
18.9%
ValueCountFrequency (%)
S7023
27.3%
F2651
 
10.3%
M1912
 
7.4%
A1870
 
7.3%
L1652
 
6.4%
H1522
 
5.9%
C1511
 
5.9%
B1071
 
4.2%
R1037
 
4.0%
T873
 
3.4%
Other values (13)4640
18.0%
ValueCountFrequency (%)
.297
41.5%
&218
30.5%
'200
28.0%
ValueCountFrequency (%)
14561
100.0%
ValueCountFrequency (%)
(74
100.0%
ValueCountFrequency (%)
)74
100.0%
ValueCountFrequency (%)
-155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin153528
90.8%
Common15579
 
9.2%

Most frequent character per script

ValueCountFrequency (%)
i15414
 
10.0%
e13471
 
8.8%
a13387
 
8.7%
d10475
 
6.8%
t10045
 
6.5%
m9691
 
6.3%
u9176
 
6.0%
o7951
 
5.2%
l7586
 
4.9%
S7023
 
4.6%
Other values (37)49309
32.1%
ValueCountFrequency (%)
14561
93.5%
.297
 
1.9%
&218
 
1.4%
'200
 
1.3%
-155
 
1.0%
(74
 
0.5%
)74
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII169107
100.0%

Most frequent character per block

ValueCountFrequency (%)
i15414
 
9.1%
14561
 
8.6%
e13471
 
8.0%
a13387
 
7.9%
d10475
 
6.2%
t10045
 
5.9%
m9691
 
5.7%
u9176
 
5.4%
o7951
 
4.7%
l7586
 
4.5%
Other values (44)57350
33.9%

address
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
1 MetLife Stadium Dr, East Rutherford, NJ
 
634
1 Patriot Pl, Foxborough, MA
 
352
1701 Bryant St, Denver, CO
 
345
1265 Lombardi Ave, Green Bay, WI
 
342
1 Arrowhead Dr, Kansas City, MO
 
339
Other values (65)
7713 

Length

Max length87
Median length33
Mean length33.50683805
Min length12

Characters and Unicode

Total characters325854
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4201 North Dale Mabry Highway, Tampa, Florida
2nd row1 Bills Dr, Orchard Park, NY
3rd row1410 Museum Campus Dr, Chicago, IL
4th row1701 Bryant St, Denver, CO
5th row1 Arrowhead Dr, Kansas City, MO
ValueCountFrequency (%)
1 MetLife Stadium Dr, East Rutherford, NJ 634
 
6.5%
1 Patriot Pl, Foxborough, MA 352
 
3.6%
1701 Bryant St, Denver, CO 345
 
3.5%
1265 Lombardi Ave, Green Bay, WI 342
 
3.5%
1 Arrowhead Dr, Kansas City, MO 339
 
3.5%
1 Bills Dr, Orchard Park, NY 330
 
3.4%
1500 Sugar Bowl Dr, New Orleans, LA 329
 
3.4%
1410 Museum Campus Dr, Chicago, IL 323
 
3.3%
9449 Friars Rd, San Diego, CA 306
 
3.1%
490 Jamestown Ave, San Francisco, CA 290
 
3.0%
Other values (60)6135
63.1%
2021-04-16T12:01:30.592429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr2855
 
5.0%
12726
 
4.8%
st1826
 
3.2%
ave1499
 
2.6%
ca1168
 
2.0%
s1065
 
1.9%
way1056
 
1.8%
stadium804
 
1.4%
east800
 
1.4%
fl725
 
1.3%
Other values (253)42705
74.6%

Most occurring characters

ValueCountFrequency (%)
56955
17.5%
a20207
 
6.2%
,19053
 
5.8%
e16783
 
5.2%
r15599
 
4.8%
t14390
 
4.4%
i13030
 
4.0%
n12019
 
3.7%
o11687
 
3.6%
l9966
 
3.1%
Other values (58)136165
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter165531
50.8%
Uppercase Letter57688
 
17.7%
Space Separator56958
 
17.5%
Decimal Number26229
 
8.0%
Other Punctuation19430
 
6.0%
Control9
 
< 0.1%
Connector Punctuation9
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A6845
11.9%
S6223
 
10.8%
D4772
 
8.3%
C4619
 
8.0%
M4038
 
7.0%
N3226
 
5.6%
P3063
 
5.3%
L2918
 
5.1%
F2554
 
4.4%
O2425
 
4.2%
Other values (15)17005
29.5%
ValueCountFrequency (%)
a20207
12.2%
e16783
10.1%
r15599
9.4%
t14390
 
8.7%
i13030
 
7.9%
n12019
 
7.3%
o11687
 
7.1%
l9966
 
6.0%
s8309
 
5.0%
d6438
 
3.9%
Other values (14)37103
22.4%
ValueCountFrequency (%)
07963
30.4%
17730
29.5%
42356
 
9.0%
22258
 
8.6%
91591
 
6.1%
51560
 
5.9%
3975
 
3.7%
7911
 
3.5%
8539
 
2.1%
6346
 
1.3%
ValueCountFrequency (%)
,19053
98.1%
.289
 
1.5%
&88
 
0.5%
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
ValueCountFrequency (%)
56955
> 99.9%
 3
 
< 0.1%
ValueCountFrequency (%)
_9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin223219
68.5%
Common102635
31.5%

Most frequent character per script

ValueCountFrequency (%)
a20207
 
9.1%
e16783
 
7.5%
r15599
 
7.0%
t14390
 
6.4%
i13030
 
5.8%
n12019
 
5.4%
o11687
 
5.2%
l9966
 
4.5%
s8309
 
3.7%
A6845
 
3.1%
Other values (39)94384
42.3%
ValueCountFrequency (%)
56955
55.5%
,19053
 
18.6%
07963
 
7.8%
17730
 
7.5%
42356
 
2.3%
22258
 
2.2%
91591
 
1.6%
51560
 
1.5%
3975
 
0.9%
7911
 
0.9%
Other values (9)1283
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII325842
> 99.9%
None12
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
56955
17.5%
a20207
 
6.2%
,19053
 
5.8%
e16783
 
5.2%
r15599
 
4.8%
t14390
 
4.4%
i13030
 
4.0%
n12019
 
3.7%
o11687
 
3.6%
l9966
 
3.1%
Other values (54)136153
41.8%
ValueCountFrequency (%)
3
25.0%
 3
25.0%
3
25.0%
3
25.0%

stadium_neutral
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9676 
True
 
49
ValueCountFrequency (%)
False9676
99.5%
True49
 
0.5%
2021-04-16T12:01:30.679074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

dt_for_home
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.60972704
Minimum0
Maximum5448.926695
Zeros9676
Zeros (%)99.5%
Memory size76.1 KiB
2021-04-16T12:01:30.854663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5448.926695
Range5448.926695
Interquartile range (IQR)0

Descriptive statistics

Standard deviation207.586132
Coefficient of variation (CV)17.88036284
Kurtosis476.7534646
Mean11.60972704
Median Absolute Deviation (MAD)0
Skewness21.07824614
Sum112904.5955
Variance43092.00219
MonotocityNot monotonic
2021-04-16T12:01:30.971009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
09676
99.5%
58.13918365
 
0.1%
4263.2319815
 
0.1%
1887.0713112
 
< 0.1%
5358.7107872
 
< 0.1%
1588.6589942
 
< 0.1%
5446.3122712
 
< 0.1%
4428.5277732
 
< 0.1%
5448.9266952
 
< 0.1%
1230.0033531
 
< 0.1%
Other values (26)26
 
0.3%
ValueCountFrequency (%)
09676
99.5%
8.7442982191
 
< 0.1%
27.676572881
 
< 0.1%
58.13918365
 
0.1%
501.9017311
 
< 0.1%
ValueCountFrequency (%)
5448.9266952
< 0.1%
5446.3122712
< 0.1%
5358.7107872
< 0.1%
4441.2057821
< 0.1%
4428.5277732
< 0.1%

dt_for_away
Real number (ℝ≥0)

Distinct1534
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.414841
Minimum1
Maximum5277.157425
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:31.089475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile182.5727665
Q1462.1027044
median872.6534573
Q31371.338114
95-th percentile2301.123231
Maximum5277.157425
Range5276.157425
Interquartile range (IQR)909.2354094

Descriptive statistics

Standard deviation664.5236289
Coefficient of variation (CV)0.6629227758
Kurtosis0.312291373
Mean1002.414841
Median Absolute Deviation (MAD)443.2145362
Skewness0.8356479375
Sum9748484.329
Variance441591.6534
MonotocityNot monotonic
2021-04-16T12:01:31.219625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.277677949
 
0.5%
82.6434508549
 
0.5%
286.159470247
 
0.5%
1371.33811446
 
0.5%
170.305288745
 
0.5%
1094.53915945
 
0.5%
258.193156244
 
0.5%
284.968323143
 
0.4%
1234.38861143
 
0.4%
1174.3497143
 
0.4%
Other values (1524)9271
95.3%
ValueCountFrequency (%)
122
0.2%
26.686854372
 
< 0.1%
32.25485441
 
< 0.1%
82.6434508549
0.5%
82.8790351722
0.2%
ValueCountFrequency (%)
5277.1574251
< 0.1%
4850.9940141
< 0.1%
4795.703111
< 0.1%
4628.6532151
< 0.1%
4187.0811331
< 0.1%

bearing_away
Real number (ℝ≥0)

Distinct1537
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.2859382
Minimum0.3504726315
Maximum359.9050725
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:31.348351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.3504726315
5-th percentile27.64674127
Q178.08752511
median180.2734436
Q3274.6841487
95-th percentile334.288612
Maximum359.9050725
Range359.5545999
Interquartile range (IQR)196.5966236

Descriptive statistics

Standard deviation103.88131
Coefficient of variation (CV)0.5826668725
Kurtosis-1.452292995
Mean178.2859382
Median Absolute Deviation (MAD)97.58052545
Skewness0.002496574268
Sum1733830.749
Variance10791.32657
MonotocityNot monotonic
2021-04-16T12:01:31.476219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.3338964949
 
0.5%
43.7053000649
 
0.5%
118.373989747
 
0.5%
59.6757191846
 
0.5%
240.460910345
 
0.5%
17.2357948445
 
0.5%
95.480300144
 
0.5%
22.1096535643
 
0.4%
301.482271343
 
0.4%
3.54558675143
 
0.4%
Other values (1527)9271
95.3%
ValueCountFrequency (%)
0.350472631520
0.2%
0.35164382353
 
< 0.1%
0.54232780365
 
0.1%
0.5668022926
 
0.1%
1.31335735113
0.1%
ValueCountFrequency (%)
359.90507251
 
< 0.1%
359.8718142
 
< 0.1%
359.82892788
0.1%
359.76138888
0.1%
359.7599892
 
< 0.1%

bearing_home
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.682486971
Minimum0
Maximum333.8636947
Zeros9676
Zeros (%)99.5%
Memory size76.1 KiB
2021-04-16T12:01:31.602716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum333.8636947
Range333.8636947
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.05226546
Coefficient of variation (CV)17.65933413
Kurtosis512.9579011
Mean0.682486971
Median Absolute Deviation (MAD)0
Skewness21.69803775
Sum6637.185793
Variance145.2571027
MonotocityNot monotonic
2021-04-16T12:01:31.715196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
09676
99.5%
44.401985295
 
0.1%
333.86369475
 
0.1%
34.083998782
 
< 0.1%
43.00212452
 
< 0.1%
62.434938532
 
< 0.1%
32.602089392
 
< 0.1%
126.01628732
 
< 0.1%
34.17888792
 
< 0.1%
149.06126781
 
< 0.1%
Other values (26)26
 
0.3%
ValueCountFrequency (%)
09676
99.5%
30.289955981
 
< 0.1%
32.602089392
 
< 0.1%
34.083998782
 
< 0.1%
34.17888792
 
< 0.1%
ValueCountFrequency (%)
333.86369475
0.1%
287.80859561
 
< 0.1%
280.31782471
 
< 0.1%
273.86187381
 
< 0.1%
268.87264261
 
< 0.1%

compass_away
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
NE
2967 
NW
2662 
SW
2209 
SE
1887 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters19450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSE
2nd rowNE
3rd rowSE
4th rowNW
5th rowNW
ValueCountFrequency (%)
NE2967
30.5%
NW2662
27.4%
SW2209
22.7%
SE1887
19.4%
2021-04-16T12:01:31.918541image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:01:31.980156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne2967
30.5%
nw2662
27.4%
sw2209
22.7%
se1887
19.4%

Most occurring characters

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter19450
100.0%

Most frequent character per category

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring scripts

ValueCountFrequency (%)
Latin19450
100.0%

Most frequent character per script

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII19450
100.0%

Most frequent character per block

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

compass_home
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)8.2%
Missing9676
Missing (%)99.5%
Memory size76.1 KiB
NE
23 
SE
12 
NW
SW

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters98
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNE
2nd rowSE
3rd rowNE
4th rowSE
5th rowNW
ValueCountFrequency (%)
NE23
 
0.2%
SE12
 
0.1%
NW8
 
0.1%
SW6
 
0.1%
(Missing)9676
99.5%
2021-04-16T12:01:32.153457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:01:32.215437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne23
46.9%
se12
24.5%
nw8
 
16.3%
sw6
 
12.2%

Most occurring characters

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter98
100.0%

Most frequent character per category

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin98
100.0%

Most frequent character per script

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII98
100.0%

Most frequent character per block

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

team1
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
NE
 
355
SF
 
351
DEN
 
348
PIT
 
347
GB
 
343
Other values (26)
7981 

Length

Max length3
Median length3
Mean length2.789820051
Min length2

Characters and Unicode

Total characters27131
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowBUF
3rd rowCHI
4th rowDEN
5th rowKC
ValueCountFrequency (%)
NE355
 
3.7%
SF351
 
3.6%
DEN348
 
3.6%
PIT347
 
3.6%
GB343
 
3.5%
KC341
 
3.5%
MIA340
 
3.5%
LVR338
 
3.5%
IND338
 
3.5%
SEA336
 
3.5%
Other values (21)6288
64.7%
2021-04-16T12:01:32.423878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne355
 
3.7%
sf351
 
3.6%
den348
 
3.6%
pit347
 
3.6%
gb343
 
3.5%
kc341
 
3.5%
mia340
 
3.5%
lvr338
 
3.5%
ind338
 
3.5%
ten336
 
3.5%
Other values (21)6288
64.7%

Most occurring characters

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27131
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring scripts

ValueCountFrequency (%)
Latin27131
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII27131
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3010
11.1%
A2905
10.7%
I2643
 
9.7%
L2156
 
7.9%
E2005
 
7.4%
C1862
 
6.9%
T1657
 
6.1%
D1319
 
4.9%
B1209
 
4.5%
R1180
 
4.3%
Other values (13)7185
26.5%

team2
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
GB
 
346
TEN
 
345
PIT
 
344
IND
 
343
NE
 
343
Other values (26)
8004 

Length

Max length3
Median length3
Mean length2.792493573
Min length2

Characters and Unicode

Total characters27157
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDET
2nd rowMIA
3rd rowGB
4th rowCIN
5th rowIND
ValueCountFrequency (%)
GB346
 
3.6%
TEN345
 
3.5%
PIT344
 
3.5%
IND343
 
3.5%
NE343
 
3.5%
NYJ342
 
3.5%
SEA339
 
3.5%
KC339
 
3.5%
BUF339
 
3.5%
DEN338
 
3.5%
Other values (21)6307
64.9%
2021-04-16T12:01:32.766874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb346
 
3.6%
ten345
 
3.5%
pit344
 
3.5%
ne343
 
3.5%
ind343
 
3.5%
nyj342
 
3.5%
sea339
 
3.5%
kc339
 
3.5%
buf339
 
3.5%
den338
 
3.5%
Other values (21)6307
64.9%

Most occurring characters

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27157
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring scripts

ValueCountFrequency (%)
Latin27157
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII27157
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3011
11.1%
A2933
10.8%
I2634
 
9.7%
L2168
 
8.0%
E2000
 
7.4%
C1856
 
6.8%
T1676
 
6.2%
D1308
 
4.8%
B1228
 
4.5%
R1190
 
4.4%
Other values (13)7153
26.3%

elo1_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9627
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1508.718489
Minimum1197.301
Maximum1839.663
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:32.880543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1197.301
5-th percentile1342.2866
Q11439.76274
median1508.928
Q31577.621
95-th percentile1670.48353
Maximum1839.663
Range642.362
Interquartile range (IQR)137.8582602

Descriptive statistics

Standard deviation99.0675304
Coefficient of variation (CV)0.0656633634
Kurtosis-0.2879641969
Mean1508.718489
Median Absolute Deviation (MAD)68.854
Skewness-0.01664253106
Sum14672287.3
Variance9814.37558
MonotocityNot monotonic
2021-04-16T12:01:33.009117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13003
 
< 0.1%
1524.2912
 
< 0.1%
1485.9032
 
< 0.1%
1600.7012
 
< 0.1%
1584.0242
 
< 0.1%
1495.3032
 
< 0.1%
1617.3832
 
< 0.1%
1456.7392
 
< 0.1%
1425.5472
 
< 0.1%
1419.6982
 
< 0.1%
Other values (9617)9704
99.8%
ValueCountFrequency (%)
1197.3011
< 0.1%
1200.1291
< 0.1%
1219.337611
< 0.1%
1227.4909281
< 0.1%
1228.0561
< 0.1%
ValueCountFrequency (%)
1839.6631
< 0.1%
1831.4621
< 0.1%
1824.2241
< 0.1%
1821.8151
< 0.1%
1810.5021
< 0.1%

elo2_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9612
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507.068132
Minimum1201.561463
Maximum1825.961
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:33.144176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1201.561463
5-th percentile1343.489676
Q11439.334
median1508.481
Q31576.814
95-th percentile1663.519
Maximum1825.961
Range624.399537
Interquartile range (IQR)137.48

Descriptive statistics

Standard deviation97.18539006
Coefficient of variation (CV)0.06448639449
Kurtosis-0.356238729
Mean1507.068132
Median Absolute Deviation (MAD)68.688
Skewness-0.07509450543
Sum14656237.58
Variance9445.00004
MonotocityNot monotonic
2021-04-16T12:01:33.270157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1552.3693
 
< 0.1%
1488.0233
 
< 0.1%
1510.3592
 
< 0.1%
1503.5172
 
< 0.1%
1411.3172
 
< 0.1%
1513.7322
 
< 0.1%
1512.7072
 
< 0.1%
1484.4222
 
< 0.1%
1561.4952
 
< 0.1%
1441.3022
 
< 0.1%
Other values (9602)9703
99.8%
ValueCountFrequency (%)
1201.5614631
< 0.1%
1210.7738861
< 0.1%
1212.0181
< 0.1%
1214.9261
< 0.1%
1226.0281
< 0.1%
ValueCountFrequency (%)
1825.9611
< 0.1%
1809.4391
< 0.1%
1806.3461
< 0.1%
1785.3921
< 0.1%
1783.6351
< 0.1%

elo_prob1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9674
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5836280748
Minimum0.0709532918
Maximum0.9645780571
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:33.407693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0709532918
5-th percentile0.28779041
Q10.4666119068
median0.594092781
Q30.7102463049
95-th percentile0.8402533518
Maximum0.9645780571
Range0.8936247653
Interquartile range (IQR)0.2436343981

Descriptive statistics

Standard deviation0.167347844
Coefficient of variation (CV)0.2867371382
Kurtosis-0.5588378845
Mean0.5836280748
Median Absolute Deviation (MAD)0.121384362
Skewness-0.2727265523
Sum5675.783027
Variance0.02800530088
MonotocityNot monotonic
2021-04-16T12:01:33.544785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53551646242
 
< 0.1%
0.58559367392
 
< 0.1%
0.53164123152
 
< 0.1%
0.58517592682
 
< 0.1%
0.43939747082
 
< 0.1%
0.62582869852
 
< 0.1%
0.44020587612
 
< 0.1%
0.32457225682
 
< 0.1%
0.5439091472
 
< 0.1%
0.59845097332
 
< 0.1%
Other values (9664)9705
99.8%
ValueCountFrequency (%)
0.07095329181
< 0.1%
0.10632428181
< 0.1%
0.10750878421
< 0.1%
0.11144128951
< 0.1%
0.11409570761
< 0.1%
ValueCountFrequency (%)
0.96457805711
< 0.1%
0.95656120561
< 0.1%
0.95425416451
< 0.1%
0.95394386461
< 0.1%
0.94728713291
< 0.1%

elo_prob2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9674
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4163719252
Minimum0.03542194285
Maximum0.9290467082
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:33.686696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03542194285
5-th percentile0.1597466482
Q10.2897536951
median0.405907219
Q30.5333880932
95-th percentile0.71220959
Maximum0.9290467082
Range0.8936247653
Interquartile range (IQR)0.2436343981

Descriptive statistics

Standard deviation0.167347844
Coefficient of variation (CV)0.4019191348
Kurtosis-0.5588378845
Mean0.4163719252
Median Absolute Deviation (MAD)0.121384362
Skewness0.2727265523
Sum4049.216973
Variance0.02800530088
MonotocityNot monotonic
2021-04-16T12:01:33.817384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67542774322
 
< 0.1%
0.43547780292
 
< 0.1%
0.542615072
 
< 0.1%
0.57150540842
 
< 0.1%
0.46944109672
 
< 0.1%
0.14258562972
 
< 0.1%
0.46835876852
 
< 0.1%
0.36172064552
 
< 0.1%
0.46448353762
 
< 0.1%
0.53116389632
 
< 0.1%
Other values (9664)9705
99.8%
ValueCountFrequency (%)
0.035421942851
< 0.1%
0.043438794421
< 0.1%
0.04574583551
< 0.1%
0.046056135371
< 0.1%
0.052712867071
< 0.1%
ValueCountFrequency (%)
0.92904670821
< 0.1%
0.89367571821
< 0.1%
0.89249121581
< 0.1%
0.88855871051
< 0.1%
0.88590429241
< 0.1%

qbelo1_pre
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507.91921
Minimum1198.229025
Maximum1806.39016
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:33.950439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1198.229025
5-th percentile1347.139155
Q11441.815237
median1509.836322
Q31573.636654
95-th percentile1664.540825
Maximum1806.39016
Range608.1611351
Interquartile range (IQR)131.8214173

Descriptive statistics

Standard deviation95.54391819
Coefficient of variation (CV)0.06336143047
Kurtosis-0.2821861756
Mean1507.91921
Median Absolute Deviation (MAD)65.92825766
Skewness-0.04901712861
Sum14664514.32
Variance9128.640303
MonotocityNot monotonic
2021-04-16T12:01:34.075334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1494.9241211
 
< 0.1%
1475.520481
 
< 0.1%
1393.5389491
 
< 0.1%
1453.756841
 
< 0.1%
1552.1619721
 
< 0.1%
1426.5686521
 
< 0.1%
1457.0574861
 
< 0.1%
1375.9248411
 
< 0.1%
1456.7384471
 
< 0.1%
1484.6927711
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
1198.2290251
< 0.1%
1200.5849231
< 0.1%
1226.7468781
< 0.1%
1228.2609811
< 0.1%
1229.6751021
< 0.1%
ValueCountFrequency (%)
1806.390161
< 0.1%
1800.2565921
< 0.1%
1793.9137211
< 0.1%
1792.0622231
< 0.1%
1789.8741631
< 0.1%

qbelo2_pre
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.738767
Minimum1206.174113
Maximum1798.835806
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:34.209605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1206.174113
5-th percentile1347.798055
Q11441.904273
median1509.452957
Q31573.941991
95-th percentile1657.167627
Maximum1798.835806
Range592.6616936
Interquartile range (IQR)132.0377187

Descriptive statistics

Standard deviation93.71171232
Coefficient of variation (CV)0.06219506286
Kurtosis-0.3300392236
Mean1506.738767
Median Absolute Deviation (MAD)65.97244765
Skewness-0.1080958958
Sum14653034.51
Variance8781.885026
MonotocityNot monotonic
2021-04-16T12:01:34.335643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1610.3455551
 
< 0.1%
1655.5958541
 
< 0.1%
1372.3569121
 
< 0.1%
1458.1497511
 
< 0.1%
1496.6033791
 
< 0.1%
1532.436371
 
< 0.1%
1492.2251561
 
< 0.1%
1374.312931
 
< 0.1%
1541.7379081
 
< 0.1%
1532.4434561
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
1206.1741131
< 0.1%
1210.9042011
< 0.1%
1213.2035831
< 0.1%
1220.8380481
< 0.1%
1223.6966431
< 0.1%
ValueCountFrequency (%)
1798.8358061
< 0.1%
1795.5140491
< 0.1%
1783.2193491
< 0.1%
1783.0446491
< 0.1%
1779.0112931
< 0.1%

qb1
Categorical

HIGH CARDINALITY

Distinct411
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Tom Brady
 
175
Brett Favre
 
159
Drew Brees
 
149
Peyton Manning
 
148
Dan Marino
 
128
Other values (406)
8966 

Length

Max length18
Median length12
Mean length12.01419023
Min length8

Characters and Unicode

Total characters116838
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.4%

Sample

1st rowDoug Williams
2nd rowJoe Ferguson
3rd rowMike Phipps
4th rowNorris Weese
5th rowMike Livingston
ValueCountFrequency (%)
Tom Brady175
 
1.8%
Brett Favre159
 
1.6%
Drew Brees149
 
1.5%
Peyton Manning148
 
1.5%
Dan Marino128
 
1.3%
John Elway128
 
1.3%
Ben Roethlisberger123
 
1.3%
Philip Rivers122
 
1.3%
Eli Manning108
 
1.1%
Vinny Testaverde107
 
1.1%
Other values (401)8378
86.1%
2021-04-16T12:01:34.708855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steve445
 
2.3%
jim376
 
1.9%
matt313
 
1.6%
manning283
 
1.5%
joe268
 
1.4%
drew262
 
1.3%
ryan260
 
1.3%
jeff230
 
1.2%
dan207
 
1.1%
tom197
 
1.0%
Other values (569)16648
85.4%

Most occurring characters

ValueCountFrequency (%)
e11616
 
9.9%
9764
 
8.4%
a8625
 
7.4%
r8592
 
7.4%
n8532
 
7.3%
o6944
 
5.9%
i6097
 
5.2%
l5140
 
4.4%
t4929
 
4.2%
s3845
 
3.3%
Other values (41)42754
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter86826
74.3%
Uppercase Letter20040
 
17.2%
Space Separator9764
 
8.4%
Other Punctuation208
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e11616
13.4%
a8625
9.9%
r8592
9.9%
n8532
9.8%
o6944
 
8.0%
i6097
 
7.0%
l5140
 
5.9%
t4929
 
5.7%
s3845
 
4.4%
y2541
 
2.9%
Other values (15)19965
23.0%
ValueCountFrequency (%)
M2201
11.0%
B2176
10.9%
J1965
 
9.8%
D1662
 
8.3%
C1336
 
6.7%
S1266
 
6.3%
T1244
 
6.2%
R1205
 
6.0%
K1064
 
5.3%
P864
 
4.3%
Other values (13)5057
25.2%
ValueCountFrequency (%)
'113
54.3%
.95
45.7%
ValueCountFrequency (%)
9764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin106866
91.5%
Common9972
 
8.5%

Most frequent character per script

ValueCountFrequency (%)
e11616
 
10.9%
a8625
 
8.1%
r8592
 
8.0%
n8532
 
8.0%
o6944
 
6.5%
i6097
 
5.7%
l5140
 
4.8%
t4929
 
4.6%
s3845
 
3.6%
y2541
 
2.4%
Other values (38)40005
37.4%
ValueCountFrequency (%)
9764
97.9%
'113
 
1.1%
.95
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII116838
100.0%

Most frequent character per block

ValueCountFrequency (%)
e11616
 
9.9%
9764
 
8.4%
a8625
 
7.4%
r8592
 
7.4%
n8532
 
7.3%
o6944
 
5.9%
i6097
 
5.2%
l5140
 
4.4%
t4929
 
4.2%
s3845
 
3.3%
Other values (41)42754
36.6%

qb2
Categorical

HIGH CARDINALITY

Distinct420
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Tom Brady
 
158
Brett Favre
 
157
Drew Brees
 
144
Peyton Manning
 
138
Philip Rivers
 
126
Other values (415)
9002 

Length

Max length18
Median length12
Mean length12.03053985
Min length8

Characters and Unicode

Total characters116997
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.5%

Sample

1st rowJoe Reed
2nd rowBob Griese
3rd rowDavid Whitehurst
4th rowKen Anderson
5th rowBert Jones
ValueCountFrequency (%)
Tom Brady158
 
1.6%
Brett Favre157
 
1.6%
Drew Brees144
 
1.5%
Peyton Manning138
 
1.4%
Philip Rivers126
 
1.3%
Dan Marino125
 
1.3%
Ben Roethlisberger121
 
1.2%
John Elway120
 
1.2%
Vinny Testaverde107
 
1.1%
Matt Ryan106
 
1.1%
Other values (410)8423
86.6%
2021-04-16T12:01:34.962085image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steve454
 
2.3%
jim379
 
1.9%
matt329
 
1.7%
joe274
 
1.4%
manning267
 
1.4%
ryan264
 
1.4%
drew260
 
1.3%
jeff246
 
1.3%
dan205
 
1.1%
tom180
 
0.9%
Other values (573)16631
85.3%

Most occurring characters

ValueCountFrequency (%)
e11689
 
10.0%
9764
 
8.3%
a8627
 
7.4%
r8620
 
7.4%
n8432
 
7.2%
o6973
 
6.0%
i6066
 
5.2%
l5238
 
4.5%
t4927
 
4.2%
s3874
 
3.3%
Other values (43)42787
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter87005
74.4%
Uppercase Letter20027
 
17.1%
Space Separator9764
 
8.3%
Other Punctuation201
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e11689
13.4%
a8627
9.9%
r8620
9.9%
n8432
9.7%
o6973
 
8.0%
i6066
 
7.0%
l5238
 
6.0%
t4927
 
5.7%
s3874
 
4.5%
y2509
 
2.9%
Other values (16)20050
23.0%
ValueCountFrequency (%)
M2202
11.0%
B2164
10.8%
J1964
 
9.8%
D1663
 
8.3%
C1339
 
6.7%
S1303
 
6.5%
R1202
 
6.0%
T1190
 
5.9%
K1068
 
5.3%
P831
 
4.1%
Other values (14)5101
25.5%
ValueCountFrequency (%)
'109
54.2%
.92
45.8%
ValueCountFrequency (%)
9764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107032
91.5%
Common9965
 
8.5%

Most frequent character per script

ValueCountFrequency (%)
e11689
 
10.9%
a8627
 
8.1%
r8620
 
8.1%
n8432
 
7.9%
o6973
 
6.5%
i6066
 
5.7%
l5238
 
4.9%
t4927
 
4.6%
s3874
 
3.6%
y2509
 
2.3%
Other values (40)40077
37.4%
ValueCountFrequency (%)
9764
98.0%
'109
 
1.1%
.92
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII116997
100.0%

Most frequent character per block

ValueCountFrequency (%)
e11689
 
10.0%
9764
 
8.3%
a8627
 
7.4%
r8620
 
7.4%
n8432
 
7.2%
o6973
 
6.0%
i6066
 
5.2%
l5238
 
4.5%
t4927
 
4.2%
s3874
 
3.3%
Other values (43)42787
36.6%

qb1_value_pre
Real number (ℝ)

Distinct9671
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.5766828
Minimum-53.77891723
Maximum317.472758
Zeros33
Zeros (%)0.3%
Memory size76.1 KiB
2021-04-16T12:01:35.074316image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-53.77891723
5-th percentile19.93142791
Q160.39579427
median98.42855351
Q3142.2045079
95-th percentile213.3488834
Maximum317.472758
Range371.2516753
Interquartile range (IQR)81.80871365

Descriptive statistics

Standard deviation58.95798196
Coefficient of variation (CV)0.5637775114
Kurtosis-0.01468244333
Mean104.5766828
Median Absolute Deviation (MAD)40.5139525
Skewness0.5042780088
Sum1017008.24
Variance3476.043637
MonotocityNot monotonic
2021-04-16T12:01:35.203121image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.3%
112.901587
 
0.1%
103.379764
 
< 0.1%
42.0773762
 
< 0.1%
66.199322
 
< 0.1%
32.646242
 
< 0.1%
16.323122
 
< 0.1%
28.112042
 
< 0.1%
52.14332
 
< 0.1%
112.448162
 
< 0.1%
Other values (9661)9667
99.4%
ValueCountFrequency (%)
-53.778917231
< 0.1%
-44.98544371
< 0.1%
-39.036960521
< 0.1%
-38.106327041
< 0.1%
-35.867069081
< 0.1%
ValueCountFrequency (%)
317.4727581
< 0.1%
313.82838351
< 0.1%
308.7623811
< 0.1%
306.84413241
< 0.1%
305.25446191
< 0.1%

qb2_value_pre
Real number (ℝ)

Distinct9690
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.4098118
Minimum-45.31072334
Maximum327.7165449
Zeros26
Zeros (%)0.3%
Memory size76.1 KiB
2021-04-16T12:01:35.342716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-45.31072334
5-th percentile20.31476558
Q161.02975275
median97.59695147
Q3141.4013777
95-th percentile212.9138725
Maximum327.7165449
Range373.0272682
Interquartile range (IQR)80.37162497

Descriptive statistics

Standard deviation58.4171454
Coefficient of variation (CV)0.5594986179
Kurtosis-0.02450860585
Mean104.4098118
Median Absolute Deviation (MAD)39.79824715
Skewness0.504049428
Sum1015385.42
Variance3412.562877
MonotocityNot monotonic
2021-04-16T12:01:35.473996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026
 
0.3%
112.901585
 
0.1%
23.124422
 
< 0.1%
108.82082
 
< 0.1%
74.81432
 
< 0.1%
61.289429142
 
< 0.1%
111.994742
 
< 0.1%
68.0132
 
< 0.1%
99.260917651
 
< 0.1%
86.224069881
 
< 0.1%
Other values (9680)9680
99.5%
ValueCountFrequency (%)
-45.310723341
< 0.1%
-36.567947131
< 0.1%
-34.949901641
< 0.1%
-33.932203311
< 0.1%
-29.90721051
< 0.1%
ValueCountFrequency (%)
327.71654491
< 0.1%
310.1306781
< 0.1%
307.03426451
< 0.1%
300.76185811
< 0.1%
300.23408331
< 0.1%

qbelo_prob1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5759398122
Minimum0.05981049406
Maximum0.9671966037
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:35.619260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.05981049406
5-th percentile0.2759039736
Q10.4540434789
median0.5874577138
Q30.7083993485
95-th percentile0.8397586078
Maximum0.9671966037
Range0.9073861096
Interquartile range (IQR)0.2543558696

Descriptive statistics

Standard deviation0.1721991186
Coefficient of variation (CV)0.2989880452
Kurtosis-0.5921599876
Mean0.5759398122
Median Absolute Deviation (MAD)0.1273096889
Skewness-0.2569352478
Sum5601.014674
Variance0.02965253645
MonotocityNot monotonic
2021-04-16T12:01:35.755565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61533884411
 
< 0.1%
0.5111208991
 
< 0.1%
0.73773042751
 
< 0.1%
0.59439835351
 
< 0.1%
0.66972782971
 
< 0.1%
0.51116901561
 
< 0.1%
0.86360311181
 
< 0.1%
0.60508965461
 
< 0.1%
0.75947104521
 
< 0.1%
0.851676851
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
0.059810494061
< 0.1%
0.070228847641
< 0.1%
0.086213926911
< 0.1%
0.10057017131
< 0.1%
0.10070933281
< 0.1%
ValueCountFrequency (%)
0.96719660371
< 0.1%
0.96590984211
< 0.1%
0.95657387441
< 0.1%
0.95226836461
< 0.1%
0.94839176891
< 0.1%

qbelo_prob2
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4240601878
Minimum0.03280339633
Maximum0.9401895059
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:01:35.889257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03280339633
5-th percentile0.1602413922
Q10.2916006515
median0.4125422862
Q30.5459565211
95-th percentile0.7240960264
Maximum0.9401895059
Range0.9073861096
Interquartile range (IQR)0.2543558696

Descriptive statistics

Standard deviation0.1721991186
Coefficient of variation (CV)0.4060723538
Kurtosis-0.5921599876
Mean0.4240601878
Median Absolute Deviation (MAD)0.1273096889
Skewness0.2569352478
Sum4123.985326
Variance0.02965253645
MonotocityNot monotonic
2021-04-16T12:01:36.018236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.76682454391
 
< 0.1%
0.41241506631
 
< 0.1%
0.3091691771
 
< 0.1%
0.22754658521
 
< 0.1%
0.66908004741
 
< 0.1%
0.439376451
 
< 0.1%
0.36650857861
 
< 0.1%
0.37818796591
 
< 0.1%
0.40307777441
 
< 0.1%
0.2723251961
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
0.032803396331
< 0.1%
0.034090157931
< 0.1%
0.043426125611
< 0.1%
0.04773163541
< 0.1%
0.051608231111
< 0.1%
ValueCountFrequency (%)
0.94018950591
< 0.1%
0.92977115241
< 0.1%
0.91378607311
< 0.1%
0.89942982871
< 0.1%
0.89929066721
< 0.1%

home_fav
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
1
6506 
0
3219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9725
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
16506
66.9%
03219
33.1%
2021-04-16T12:01:36.224413image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:01:36.285054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring characters

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9725
100.0%

Most frequent character per category

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring scripts

ValueCountFrequency (%)
Common9725
100.0%

Most frequent character per script

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9725
100.0%

Most frequent character per block

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Interactions

2021-04-16T12:00:44.778663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:44.887545image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:44.990779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.095977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.199293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.391230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.501081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.607522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.722887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.834593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:45.942304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.041589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.140825image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.246731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.352518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.460176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.567523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.668596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.768914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.868926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:46.970013image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.073130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.172741image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.270936image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.369770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.472100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.578277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.682927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.886449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:47.983274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.082645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.186299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.288523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.392995image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.496993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.595412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.693724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.799313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:48.904311image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.010455image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.116256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.221129image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.325058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.434399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.544516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.650782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.759078image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.861448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:49.967365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.076381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.184730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.401037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.508377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.611485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.713966image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.819597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:50.922477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.027404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.132637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.236792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.341221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.449776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.560807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.670199image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.777808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.881042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:51.985258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.096940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.206779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.314567image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.421999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.527895image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.630400image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.731820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:52.930372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.036083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.138504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.240104image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.341692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.447338image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.556496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.660665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.768752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.867900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:53.969808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.074447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.182099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.289945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.395785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.498040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.605232image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:00:54.717587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-04-16T12:01:17.269823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.377619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.488624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.602089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.755830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.875053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:17.988665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.097808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.208007image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.466777image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.611616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.740536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.849344image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:18.961713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.064117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.166755image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.269561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.374152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.474156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.578000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.680634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.790339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:19.901368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.006099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.117566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.217288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.315645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.417619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.550702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.707509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.848977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:20.958293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.065943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.288098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.392176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.509516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.616337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.721347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.826708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:21.931691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.036955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.138409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.238003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.336241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.432204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.534565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.636509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.740221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:01:22.841701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-16T12:01:36.480689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-16T12:01:36.735359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-16T12:01:36.978815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-16T12:01:37.257261image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-16T12:01:37.576069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-16T12:01:23.173616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-16T12:01:24.367744image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-16T12:01:24.677785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexgame_iddate_stringschedule_dateschedule_seasonschedule_weekschedule_playoffhome_team_idhome_cityhome_teamnameaway_cityaway_teamnameaway_team_idresultteam_homescore_homescore_awayteam_awayteam_favorite_idspread_favoriteover_under_linestadiumaddressstadium_neutraldt_for_homedt_for_awaybearing_awaybearing_homecompass_awaycompass_hometeam1team2elo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
0229819790901TBDETSat Sep 01, 19791979-09-0119791FalseTBTampa BayBuccaneersDetroitLionsDET1Tampa Bay Buccaneers3116Detroit LionsTB-3.030Houlihan's Stadium4201 North Dale Mabry Highway, Tampa, FloridaFalse0.0988.905373178.0621570.0SENaNTBDET1385.2041487.0390.4471880.5528121382.9280351501.886317Doug WilliamsJoe Reed35.51671855.1599810.4271680.5728321
1229919790902BUFMIASun Sep 02, 19791979-09-0219791FalseBUFBuffaloBillsMiamiDolphinsMIA0Buffalo Bills79Miami DolphinsMIA-5.039Ralph Wilson Stadium1 Bills Dr, Orchard Park, NYFalse0.01174.3497103.5455870.0NENaNBUFMIA1422.8201573.5130.3791190.6208811423.2393531559.950522Joe FergusonBob Griese58.06348596.1223450.3753530.6246470
2230019790902CHIGBSun Sep 02, 19791979-09-0219791FalseCHIChicagoBearsGreen BayPackersGB1Chicago Bears63Green Bay PackersCHI-3.031Soldier Field1410 Museum Campus Dr, Chicago, ILFalse0.0184.036793173.6262700.0SENaNCHIGB1485.8061462.5020.6244120.3755881483.9094901464.019768Mike PhippsDavid Whitehurst42.75679141.4946680.6083960.3916041
3230119790902DENCINSun Sep 02, 19791979-09-0219791FalseDENDenverBroncosCincinnatiBengalsCIN1Denver Broncos100Cincinnati BengalsDEN-3.031.5Mile High Stadium1701 Bryant St, Denver, COFalse0.01095.723761278.8417280.0NWNaNDENCIN1579.1211491.3800.7066660.2933341580.3486261487.024327Norris WeeseKen Anderson21.70088890.9294080.6756020.3243981
4230219790902KCINDSun Sep 02, 19791979-09-0219791FalseKCKansas CityChiefsBaltimoreColtsIND1Kansas City Chiefs140Baltimore ColtsKC-1.037Arrowhead Stadium1 Arrowhead Dr, Kansas City, MOFalse0.0958.341925274.6841490.0NWNaNKCIND1408.0821420.1600.5755770.4244231411.6477421424.210902Mike LivingstonBert Jones49.196730121.4144960.5280070.4719931
5230319790902LARLVRSun Sep 02, 19791979-09-0219791FalseLARLos AngelesRamsOaklandRaidersLVR0Los Angeles Rams1724Oakland RaidersLAR-4.036.5Anaheim Stadium2000 E Gene Autry Way, Anaheim, CAFalse0.0369.669204137.0034890.0SENaNLARLVR1571.8371543.1080.6317070.3682931570.5303501554.746347Pat HadenKen Stabler79.88982839.5395310.6175160.3824841
6230419790902MINSFSun Sep 02, 19791979-09-0219791FalseMINMinneapolisVikingsSan Francisco49ersSF1Minnesota Vikings2822San Francisco 49ersMIN-7.032Metropolitan Stadium8000 Cedar Avenue South, Bloomington, MinnesotaFalse0.01584.37377862.7880160.0NENaNMINSF1501.0701376.8950.7481890.2518111501.3926971385.556901Tommy KramerSteve DeBerg16.15214514.9506340.7075200.2924801
7230519790902NOATLSun Sep 02, 19791979-09-0219791FalseNONew OrleansSaintsAtlantaFalconsATL0New Orleans Saints3440Atlanta FalconsNO-5.032Louisiana Superdome1500 Sugar Bowl Dr, New Orleans, LAFalse0.0424.760553233.5164140.0SWNaNNOATL1454.5411472.7480.5669360.4330641447.1595451464.242458Archie ManningSteve Bartkowski135.57509457.5117630.5631440.4368561
8230619790902NYJCLESun Sep 02, 19791979-09-0219791FalseNYJNew YorkJetsClevelandBrownsCLE0New York Jets2225Cleveland BrownsNYJ-2.041Metlife Stadium1 MetLife Stadium Dr, East Rutherford, NJFalse0.0400.08190494.3298620.0SENaNNYJCLE1486.7031483.7340.5965860.4034141491.2734431479.160062Matt RobinsonBrian Sipe32.187388118.0722330.5789640.4210361
9230719790902PHINYGSun Sep 02, 19791979-09-0219791FalsePHIPhiladelphiaEaglesNew YorkGiantsNYG1Philadelphia Eagles2317New York GiantsPHI-7.031.5Veterans Stadium3501 South Broad Street, Philadelphia, PennsylvaniaFalse0.082.958831228.3546200.0SWNaNPHINYG1516.2851435.8620.6978590.3021411515.4011981430.892805Ron JaworskiJoe Pisarcik68.93902947.9364080.6862890.3137111

Last rows

df_indexgame_iddate_stringschedule_dateschedule_seasonschedule_weekschedule_playoffhome_team_idhome_cityhome_teamnameaway_cityaway_teamnameaway_team_idresultteam_homescore_homescore_awayteam_awayteam_favorite_idspread_favoriteover_under_linestadiumaddressstadium_neutraldt_for_homedt_for_awaybearing_awaybearing_homecompass_awaycompass_hometeam1team2elo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
97151201320210110NOCHISun Jan 10, 20212021-01-102020WildcardTrueNONew OrleansSaintsChicagoBearsCHI1New Orleans Saints219Chicago BearsNO-11.048Mercedes-Benz Superdome1500 Sugar Bowl Dr, New Orleans, LAFalse0.0833.524038190.2612340.0SWNaNNOCHI1695.6835991500.1184650.8175650.1824351730.5346381497.160456Drew BreesMitchell Trubisky222.286925164.8393310.8498200.1501801
97161201420210110PITCLESun Jan 10, 20212021-01-102020WildcardTruePITPittsburghSteelersClevelandBrownsCLE0Pittsburgh Steelers3748Cleveland BrownsPIT-5.547.5Heinz Field100 Art Rooney Ave, Pittsburgh, PAFalse0.0114.228939129.1938270.0SENaNPITCLE1572.1614421516.9817690.6663690.3336311578.4457221536.927531Ben RoethlisbergerBaker Mayfield204.930263164.4893120.6194430.3805571
97171201520210110TENBALSun Jan 10, 20212021-01-102020WildcardTrueTENTennesseeTitansBaltimoreRavensBAL0Tennessee Titans1320Baltimore RavensBAL-3.553.5Nissan Stadium1 Titans Way, Nashville, TNFalse0.0596.492606251.9921330.0SWNaNTENBAL1599.0765991654.2150040.5141880.4858121571.6963931651.052726Ryan TannehillLamar Jackson216.955557249.5656730.4257140.5742860
97181201620210116BUFBALSat Jan 16, 20212021-01-162020DivisionTrueBUFBuffaloBillsBaltimoreRavensBAL1Buffalo Bills173Baltimore RavensBUF-2.549.5New Era Field1 Bills Dr, Orchard Park, NYFalse0.0265.725548335.3828930.0NWNaNBUFBAL1700.5380091675.6957770.6264870.3735131688.2529861668.348824Josh AllenLamar Jackson289.086698243.1852250.6528860.3471141
97191201720210116GBLARSat Jan 16, 20212021-01-162020DivisionTrueGBGreen BayPackersLos AngelesRamsLAR1Green Bay Packers3218Los Angeles RamsGB-7.045Lambeau Field1265 Lombardi Ave, Green Bay, WIFalse0.01758.30052656.7915650.0NENaNGBLAR1700.2260621620.4985350.6970140.3029861674.7860931633.700632Aaron RodgersJared Goff266.649955161.6745290.7108750.2891251
97201201820210117KCCLESun Jan 17, 20212021-01-172020DivisionTrueKCKansas CityChiefsClevelandBrownsCLE1Kansas City Chiefs2217Cleveland BrownsKC-8.056Arrowhead Stadium1 Arrowhead Dr, Kansas City, MOFalse0.0696.123935260.1274970.0SWNaNKCCLE1712.6520901552.0127250.7856470.2143531711.2170291568.944343Patrick MahomesBaker Mayfield273.850726182.7177660.7899270.2100731
97211201920210117NOTBSun Jan 17, 20212021-01-172020DivisionTrueNONew OrleansSaintsTampa BayBuccaneersTB0New Orleans Saints2030Tampa Bay BuccaneersNO-2.553Mercedes-Benz Superdome1500 Sugar Bowl Dr, New Orleans, LAFalse0.0481.270463290.6705060.0NWNaNNOTB1704.0512941645.0740080.6712120.3287881737.3112961624.424406Drew BreesTom Brady226.770860221.7779060.7058590.2941411
97221202020210124GBTBSun Jan 24, 20212021-01-242020ConferenceTrueGBGreen BayPackersTampa BayBuccaneersTB0Green Bay Packers2631Tampa Bay BuccaneersGB-3.053Lambeau Field1265 Lombardi Ave, Green Bay, WIFalse0.01198.450281346.7346340.0NWNaNGBTB1715.6231871679.1862550.6419680.3580321689.4067921660.789489Aaron RodgersTom Brady276.653962221.7470910.6291910.3708091
97231202120210124KCBUFSun Jan 24, 20212021-01-242020ConferenceTrueKCKansas CityChiefsBuffaloBillsBUF1Kansas City Chiefs3824Buffalo BillsKC-3.055Arrowhead Stadium1 Arrowhead Dr, Kansas City, MOFalse0.0856.619992257.2687830.0SWNaNKCBUF1719.6189211719.9741490.5919720.4080281718.0322471706.159766Patrick MahomesJosh Allen271.760879273.2900460.5356370.4643631
97241202220210207TBKCSun Feb 07, 20212021-02-072020SuperbowlTrueTBTampa BayBuccaneersKansas CityChiefsKC1Tampa Bay Buccaneers319Kansas City ChiefsKC-3.056Raymond James Stadium4201 N Dale Mabry Hwy, Tampa, FLFalse0.01033.075041134.1819580.0SENaNTBKC1703.3032961741.0872790.5390870.4609131684.3190581742.902172Tom BradyPatrick Mahomes211.680227282.2613260.4662060.5337940